Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm
The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manu...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/5/1280 |
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| author | Wei Wu Xiaochun Zhong Chaokai Lei Yuanyuan Zhao Tao Liu Chengming Sun Wenshan Guo Tan Sun Shengping Liu |
| author_facet | Wei Wu Xiaochun Zhong Chaokai Lei Yuanyuan Zhao Tao Liu Chengming Sun Wenshan Guo Tan Sun Shengping Liu |
| author_sort | Wei Wu |
| collection | DOAJ |
| description | The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manual surveys, which are time-consuming, laborious, inefficient and inaccurate. Existing non-destructive wheat ear detection techniques are mostly applied to near-ground images and are difficult to apply to large-scale monitoring. In this study, we proposed a sampling survey method based on the unmanned aerial vehicle (UAV). Firstly, a small number of UAV images were acquired based on the five-point sampling mode. Secondly, an adaptive Gaussian kernel size was used to generate the ground truth density map. Thirdly, a density map regression network (DM-Net) was constructed and optimized. Finally, we designed an overlapping area of sub-images to solve the repeated counting caused by image segmentation. The MAE and MSE of the proposed model were 9.01 and 11.85, respectively. We compared the sampling survey method based on UAV images in this paper with the manual survey method. The results showed that the RMSE and MAPE of NM13 were 18.95 × 10<sup>4</sup>/hm<sup>2</sup> and 3.37%, respectively, and for YFM4, 13.65 × 10<sup>4</sup>/hm<sup>2</sup> and 2.94%, respectively. This study enables the investigation of the number of wheat ears in a large area, which can provide favorable support for wheat yield estimation. |
| first_indexed | 2024-03-11T07:12:30Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:12:30Z |
| publishDate | 2023-02-01 |
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| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b3005aa09be242dbaeb3c2d3d471d03f2023-11-17T08:30:54ZengMDPI AGRemote Sensing2072-42922023-02-01155128010.3390/rs15051280Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression AlgorithmWei Wu0Xiaochun Zhong1Chaokai Lei2Yuanyuan Zhao3Tao Liu4Chengming Sun5Wenshan Guo6Tan Sun7Shengping Liu8Key Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaKey Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaKey Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaKey Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaKey Laboratory of Agricultural Blockchain Application, Ministry of Agriculture and Rural Affairs, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaThe number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manual surveys, which are time-consuming, laborious, inefficient and inaccurate. Existing non-destructive wheat ear detection techniques are mostly applied to near-ground images and are difficult to apply to large-scale monitoring. In this study, we proposed a sampling survey method based on the unmanned aerial vehicle (UAV). Firstly, a small number of UAV images were acquired based on the five-point sampling mode. Secondly, an adaptive Gaussian kernel size was used to generate the ground truth density map. Thirdly, a density map regression network (DM-Net) was constructed and optimized. Finally, we designed an overlapping area of sub-images to solve the repeated counting caused by image segmentation. The MAE and MSE of the proposed model were 9.01 and 11.85, respectively. We compared the sampling survey method based on UAV images in this paper with the manual survey method. The results showed that the RMSE and MAPE of NM13 were 18.95 × 10<sup>4</sup>/hm<sup>2</sup> and 3.37%, respectively, and for YFM4, 13.65 × 10<sup>4</sup>/hm<sup>2</sup> and 2.94%, respectively. This study enables the investigation of the number of wheat ears in a large area, which can provide favorable support for wheat yield estimation.https://www.mdpi.com/2072-4292/15/5/1280density map regressionsampling surveyUAVswheat ear number |
| spellingShingle | Wei Wu Xiaochun Zhong Chaokai Lei Yuanyuan Zhao Tao Liu Chengming Sun Wenshan Guo Tan Sun Shengping Liu Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm Remote Sensing density map regression sampling survey UAVs wheat ear number |
| title | Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm |
| title_full | Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm |
| title_fullStr | Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm |
| title_full_unstemmed | Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm |
| title_short | Sampling Survey Method of Wheat Ear Number Based on UAV Images and Density Map Regression Algorithm |
| title_sort | sampling survey method of wheat ear number based on uav images and density map regression algorithm |
| topic | density map regression sampling survey UAVs wheat ear number |
| url | https://www.mdpi.com/2072-4292/15/5/1280 |
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